Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Deepti; Sahoo, Satyaban; Seth, Neha Title A name given to the resource Predicting sustainable equity indices using deep long short-term memory neural network: Evidence from developed and emerging markets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Cogent Economics and Finance;Volume;14;Issue;1;Article No.;2620875; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/23322039.2026.2620875" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/23322039.2026.2620875</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029126298?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029126298?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh D., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Delhi NCR Campus, India; Sahoo S., ManipalSchool of Commerce and Economics, Manipal Academy of Higher Education, Manipal, India; Seth N., School of Management Studies, Indira Gandhi National Open University, New Delhi, India Description An account of the resource The present study aims to propose a predictive model to forecast the sustainable stock indices. For this, the Long Short-Term Memory (LSTM) neural network model is applied through Keras and TensorFlow to closing values of six developed and emergingmarkets: the US, the UK, Japan, Brazil, South Africa, and China. Further, the Adam optimiser and mean squared error loss function are used to train the model. To gauge the superiority of the LSTM model, a rolling window Autoregressive Integrated Moving Average (ARIMA) model is also employed. The performance accuracy of both models is evaluated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The LSTM model, with two LSTM and two dense layers, yields the best results, achieving the highest precision in predicting the values of sustainable indices. The values of RMSE and MAPE confirmed this, and the accuracy is also verified by the R2 values. LSTM shows superior predictive accuracy and is indicated to be fit for non-linear market patterns than rolling window ARIMA. The study enables policymakers and practitioners to forecast these indices and design policies to motivate related investments. 2026 The Author(s). Published by Informa UK Limited, trading as Taylor &amp; Francis Group. Subject The topic of the resource ARIMA; Deep-Learning model; GRU; LSTM; neural Networks; sustainable stock indices Publisher An entity responsible for making the resource available Cogent OA Relation A related resource ISSN: 23322039; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online